research-article

Exposing digital audio forgeries in time domain by using singularity analysis with wavelets

Authors:

Hongbin HuangAuthors Info & Claims

IH&MMSec '13: Proceedings of the first ACM workshop on Information hiding and multimedia security

Pages 149 - 158

https://doi.org/10.1145/2482513.2482516

Published: 17 June 2013 Publication History

Abstract

Exposing digital audio forgeries in time domain is a significant research issue in the audio forensics community. In this paper, we develop an audio forensics method to detect and locate audio forgeries in time domain (including deletion, insertion, substitution and splicing) by analyzing singularity points of audio signals after performing discrete wavelet packet decomposition. Firstly, we observe and point out that a forgery operation in time domain will often generate a singularity point because the correlation property of those samples close to the tampering position has been degraded. Furthermore, we investigate and find that the singularity point resulted from a tampering operation often stays alone while those inherent singularity points in the original signal usually staying in the form of group. Finally, we propose an approach to expose audio forgeries in time domain by introducing Mallat et al.'s wavelet singularity analysis method and making a difference between a forged point and the inherent singularity points. Extensive experimental results have shown that the proposed scheme can better identify whether a given speech file has been tampered (e.g., part of the content deleted or replaced) previously and further locate the forged positions in time domain.

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Cited By

Narla VSuresh GK Singh MM V(2024)Speech Signal Splicing Detection system based on MFCC and DTWInternational Research Journal of Multidisciplinary Technovation10.54392/irjmt24613(186-197)Online publication date: 21-Nov-2024
https://doi.org/10.54392/irjmt24613
Lalitha Narla VSuresh GLalitha RVinod Kumar M(2024)An Intelligent System for Audio Splicing Forgery Detection Using MFCCAdvances in Signal Processing and Communication Engineering10.1007/978-981-97-0562-7_29(387-396)Online publication date: 4-Jul-2024
https://doi.org/10.1007/978-981-97-0562-7_29
Zeng ZWu Z(2022)Audio Splicing Localization: Can We Accurately Locate the Splicing Tampering?2022 13th International Symposium on Chinese Spoken Language Processing (ISCSLP)10.1109/ISCSLP57327.2022.10037855(120-124)Online publication date: 11-Dec-2022
https://doi.org/10.1109/ISCSLP57327.2022.10037855
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Index Terms

Exposing digital audio forgeries in time domain by using singularity analysis with wavelets
1. General and reference
  1. Document types

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Published In

cover image ACM Conferences

IH&MMSec '13: Proceedings of the first ACM workshop on Information hiding and multimedia security

June 2013

242 pages

ISBN:9781450320818

DOI:10.1145/2482513

General Chair:
William Puech
LIRMM & University of Montpellier, France
,
Program Chairs:
Marc Chaumont
LIRMM & University of Nimes, France
,
Jana Dittmann
Otto-von-Guericke University, Germany
,
Patrizio Campisi
University of Roma TRE, Italy

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 June 2013

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IH&MMSec '13

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SIGMM

IH&MMSec '13: ACM Information Hiding and Multimedia Security Workshop

June 17 - 19, 2013

Montpellier, France

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IH&MMSec '13 Paper Acceptance Rate 27 of 74 submissions, 36%;

Overall Acceptance Rate 128 of 318 submissions, 40%

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Cited By

Narla VSuresh GK Singh MM V(2024)Speech Signal Splicing Detection system based on MFCC and DTWInternational Research Journal of Multidisciplinary Technovation10.54392/irjmt24613(186-197)Online publication date: 21-Nov-2024
https://doi.org/10.54392/irjmt24613
Lalitha Narla VSuresh GLalitha RVinod Kumar M(2024)An Intelligent System for Audio Splicing Forgery Detection Using MFCCAdvances in Signal Processing and Communication Engineering10.1007/978-981-97-0562-7_29(387-396)Online publication date: 4-Jul-2024
https://doi.org/10.1007/978-981-97-0562-7_29
Zeng ZWu Z(2022)Audio Splicing Localization: Can We Accurately Locate the Splicing Tampering?2022 13th International Symposium on Chinese Spoken Language Processing (ISCSLP)10.1109/ISCSLP57327.2022.10037855(120-124)Online publication date: 11-Dec-2022
https://doi.org/10.1109/ISCSLP57327.2022.10037855
Zhang KLiang SNie SHe SPan JZhang XMa HYi J(2022)A Robust Deep Audio Splicing Detection Method via Singularity Detection FeatureICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP43922.2022.9746596(2919-2923)Online publication date: 23-May-2022
https://doi.org/10.1109/ICASSP43922.2022.9746596
Chuchra AKaur MGupta S(2022)A Deep Learning Approach for Splicing Detection in Digital AudiosCongress on Intelligent Systems10.1007/978-981-16-9416-5_39(543-558)Online publication date: 1-Jul-2022
https://doi.org/10.1007/978-981-16-9416-5_39
Huang XLiu ZLu WLiu HXiang S(2020)Fast and Effective Copy-Move Detection of Digital Audio Based on Auto SegmentDigital Forensics and Forensic Investigations10.4018/978-1-7998-3025-2.ch011(127-142)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-3025-2.ch011
Bevinamarad PShirldonkar M(2020)Audio Forgery Detection Techniques: Present and Past Review2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)10.1109/ICOEI48184.2020.9143014(613-618)Online publication date: Jun-2020
https://doi.org/10.1109/ICOEI48184.2020.9143014
Huang XLiu ZLu WLiu HXiang S(2019)Fast and Effective Copy-Move Detection of Digital Audio Based on Auto SegmentInternational Journal of Digital Crime and Forensics10.4018/IJDCF.201904010411:2(47-62)Online publication date: 1-Apr-2019
https://doi.org/10.4018/IJDCF.2019040104
Rouniyar SYingjuan YHu Y(2018)Channel Response Based Multi-Feature Audio Splicing Forgery Detection and LocalizationProceedings of the 2018 International Conference on E-Business, Information Management and Computer Science10.1145/3210506.3210515(46-53)Online publication date: 14-Apr-2018
https://dl.acm.org/doi/10.1145/3210506.3210515
Liu ZLu W(2017)Fast Copy-Move Detection of Digital Audio2017 IEEE Second International Conference on Data Science in Cyberspace (DSC)10.1109/DSC.2017.11(625-629)Online publication date: Jun-2017
https://doi.org/10.1109/DSC.2017.11
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